[ASR] rm transformers import and modify variable name consistent with infer.py, test=asr (#2929)

* rm transformers import and modify variable name consistent with infer.py

* add condition ctc_prefix_beam_search decode.

paddlespeech/s2t/exps/wav2vec2/model.py

@@ -23,9 +23,9 @@ from contextlib import nullcontext
 import jsonlines
 import numpy as np
 import paddle
-import transformers
 from hyperpyyaml import load_hyperpyyaml
 from paddle import distributed as dist
+from paddlenlp.transformers import AutoTokenizer
 
 from paddlespeech.s2t.frontend.featurizer import TextFeaturizer
 from paddlespeech.s2t.io.dataloader import DataLoaderFactory
@@ -530,8 +530,7 @@ class Wav2Vec2ASRTrainer(Trainer):
         datasets = [train_data, valid_data, test_data]
 
         # Defining tokenizer and loading it
-        tokenizer = transformers.BertTokenizer.from_pretrained(
-            'bert-base-chinese')
+        tokenizer = AutoTokenizer.from_pretrained('bert-base-chinese')
         self.tokenizer = tokenizer
         # 2. Define audio pipeline:
         @data_pipeline.takes("wav")
@@ -867,8 +866,7 @@ class Wav2Vec2ASRTester(Wav2Vec2ASRTrainer):
         vocab_list = self.vocab_list
         decode_batch_size = decode_cfg.decode_batch_size
 
-        with jsonlines.open(
-                self.args.result_file, 'w', encoding='utf8') as fout:
+        with jsonlines.open(self.args.result_file, 'w') as fout:
             for i, batch in enumerate(self.test_loader):
                 if self.use_sb:
                     metrics = self.sb_compute_metrics(**batch, fout=fout)

paddlespeech/s2t/models/wav2vec2/wav2vec2_ASR.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from collections import defaultdict
+from turtle import Turtle
 from typing import Dict
 from typing import List
 from typing import Tuple
@@ -83,6 +84,7 @@ class Wav2vec2ASR(nn.Layer):
                text_feature: Dict[str, int],
                decoding_method: str,
                beam_size: int,
+               tokenizer: str=None,
                sb_pipeline=False):
         batch_size = feats.shape[0]
 
@@ -93,12 +95,15 @@ class Wav2vec2ASR(nn.Layer):
             logger.error(f"current batch_size is {batch_size}")
 
         if decoding_method == 'ctc_greedy_search':
-            if not sb_pipeline:
+            if tokenizer is None and sb_pipeline is False:
                 hyps = self.ctc_greedy_search(feats)
                 res = [text_feature.defeaturize(hyp) for hyp in hyps]
                 res_tokenids = [hyp for hyp in hyps]
             else:
-                hyps = self.ctc_greedy_search(feats.unsqueeze(-1))
+                if sb_pipeline is True:
+                    hyps = self.ctc_greedy_search(feats.unsqueeze(-1))
+                else:
+                    hyps = self.ctc_greedy_search(feats)
                 res = []
                 res_tokenids = []
                 for sequence in hyps:
@@ -123,13 +128,16 @@ class Wav2vec2ASR(nn.Layer):
         # with other batch decoding mode
         elif decoding_method == 'ctc_prefix_beam_search':
             assert feats.shape[0] == 1
-            if not sb_pipeline:
+            if tokenizer is None and sb_pipeline is False:
                 hyp = self.ctc_prefix_beam_search(feats, beam_size)
                 res = [text_feature.defeaturize(hyp)]
                 res_tokenids = [hyp]
             else:
-                hyp = self.ctc_prefix_beam_search(
-                    feats.unsqueeze(-1), beam_size)
+                if sb_pipeline is True:
+                    hyp = self.ctc_prefix_beam_search(
+                        feats.unsqueeze(-1), beam_size)
+                else:
+                    hyp = self.ctc_prefix_beam_search(feats, beam_size)
                 res = []
                 res_tokenids = []
                 predicted_tokens = text_feature.convert_ids_to_tokens(hyp)

setup.py

@@ -69,7 +69,6 @@ base = [
     "paddleslim>=2.3.4",
     "paddleaudio>=1.1.0",
     "hyperpyyaml",
-    "transformers",
 ]
 
 server = ["pattern_singleton", "websockets"]